Gender
Home | Data Overview | Multi-Dimensional Analysis | Conclusion
Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care | Analysis
Function
To begin, let’s explore the function we created to run all of this statistical analysis:
mysubsetMDS <- function(x){
mysubset <- df %>%
select(starts_with(x))
meta <- metaMDS(mysubset)
MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
cbind(demo)
return(MDS_df)
}
In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring Gender.
Question Types
Science Identity
Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:
This is great and all, but let’s run an adonis test to see if there is a significant difference in how different genders responded to science identity questions:
##
## Call:
## adonis(formula = si ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.2514 0.062850 1.9936 0.02532 0.13
## Residuals 307 9.6785 0.031526 0.97468
## Total 311 9.9299 1.00000This shows that the differences in answers are not significant, according to gender.
Career Motivation
Next, let’s look into Carer Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = cm ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.0785 0.019613 0.38219 0.00495 0.678
## Residuals 307 15.7544 0.051317 0.99505
## Total 311 15.8329 1.00000This shows that the differences in answers are not significant, according to gender.
Intrinsic Motivation
Now, Intrinsic Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = im ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.0468 0.011706 0.41002 0.00531 0.63
## Residuals 307 8.7651 0.028551 0.99469
## Total 311 8.8119 1.00000This shows that the differences in answers are not significant, according to gender.
Self-Determination
Now, Self-Determination:
And an Adonis test:
##
## Call:
## adonis(formula = sd ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.0500 0.012490 0.50676 0.00656 0.631
## Residuals 307 7.5669 0.024648 0.99344
## Total 311 7.6168 1.00000This shows that the differences in answers are not significant, according to gender.
Self-Efficacy
Now, Self-Efficacy:
And an Adonis test:
##
## Call:
## adonis(formula = se ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.0710 0.017760 0.64639 0.00835 0.428
## Residuals 307 8.4349 0.027475 0.99165
## Total 311 8.5059 1.00000This shows that the differences in answers are not significant, according to gender.
Grade Motivation
Now, Grade Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = gm ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.0471 0.011778 0.52835 0.00684 0.344
## Residuals 307 6.8438 0.022292 0.99316
## Total 311 6.8909 1.00000This shows that the differences in answers are not significant, according to gender.
Competency in Science
Now, Competency in Science:
And an Adonis test:
##
## Call:
## adonis(formula = sci_comp ~ demo$gender)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$gender 4 0.3053 0.076316 2.2316 0.02825 0.055 .
## Residuals 307 10.4987 0.034198 0.97175
## Total 311 10.8040 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1This shows that the differences in answers are not significant, according to gender.
